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Record W1606731941 · doi:10.7176/nmmc.vol319-20

Cultural Studies On Emotions And Communication Skills In English

2012· article· en· W1606731941 on OpenAlexaboutno aff
H.L. Narayanarao

Bibliographic record

VenueNew media and mass communication · 2012
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAngerEthnographyPsychologyPerspective (graphical)AggressionPerceptionAffect (linguistics)Social psychologyCultural anthropologyCultural studiesEpistemologySociologyAnthropologyCommunication

Abstract

fetched live from OpenAlex

The above title of the research paper, I wish to  present as a theoretical paper. The research paper will outline and discuss a method of emotions and communication that mingled with cultural perception.  The aim of this method is to integrate recent developments  in emotions research into a  communication-theoretical framework in  the study. Cultural studies of emotions originated from  anthropology ,  sociology and  psychology . The first accounts of emotion from a cultural perspective were  ethnographic , and described emotions as idiosyncratic . Researchers such as  Margaret Mead ,  Gregory Bateson and  Jean Briggs described unique emotional phenomena and stressed emotions as  culturally determined . For example, Briggs lived among the Utku  Inuit and described a society where  anger and  aggression almost never occur, despite the common western notion that anger is a primitive universal emotion. Although these ethnographic studies point to considerable cultural differences, no general conclusions can be drawn from them regarding what cultural aspects affect emotions, or what level the culture influence. For example, it might be that the same emotions are experienced by all human beings; however the events that evoke them or the reactions they cause differ across cultures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.206
GPT teacher head0.392
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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